Retrieving　the　subsurface　and　deeper　ocean　(SDO)　dynamic　parameters　from　satellite　observations　is　crucial　for　effectively　understanding　ocean　interior　anomalies　and　dynamic　processes,　but　it　is　challenging　to　accurately　estimate　the　subsurface　thermal　structure　over　the　global　scale　from　sea　surface　parameters.　This　study　proposes　a　new　approach　based　on　Random　Forest　(RF)　machine　learning　to　retrieve　subsurface　temperature　anomaly　(STA)　in　the　global　ocean　from　multisource　satellite　observations　including　sea　surface　height　anomaly　(SSHA),　sea　surface　temperature　anomaly　(SSTA),　sea　surface　salinity　anomaly　(SSSA),　and　sea　surface　wind　anomaly　(SSWA)　via　in　situ　Argo　data　for　RF　training　and　testing.　RF　machine-learning　approach　can　accurately　retrieve　the　STA　in　the　global　ocean　from　satellite　observations　of　sea　surface　parameters　(SSHA,　SSTA,　SSSA,　SSWA).　The　Argo　STA　data　were　used　to　validate　the　accuracy　and　reliability　of　the　results　from　the　RF　model.　The　results　indicated　that　SSHA,　SSTA,　SSSA,　and　SSWA　together　are　useful　parameters　for　detecting　SDO　thermal　information　and　obtaining　accurate　STA　estimations.　The　proposed　method　also　outperformed　support　vector　regression　(SVR)　in　global　STA　estimation.　It　will　be　a　useful　technique　for　studying　SDO　thermal　variability　and　its　role　in　global　climate　system　from　global-scale　satellite　observations.